The automation of the vehicle operation, even for passenger cars and other motor vehicles used in road traffic, is an increasingly occurring equipment feature. For example, driver assistance systems are already known, which can take over the parking process for a driver and the like. It was proposed to have a motor vehicle parked fully automatically and driverlessly into a target parking space in other areas, such as dedicated parking environments, in particular a parking garage.
An essential component of vehicle systems that are designed to fully automatically guide motor vehicles, in particular in the absence of the driver, is the classification of objects detected by environmental sensors of the motor vehicle as an obstacle or not an obstacle in order to be able to plan the further trajectory of the automatic operation. Environmental sensors, such as cameras, radar sensors, Lidar sensors, ultrasonic sensors, and the like, therefore deliver sensor data that describe objects in the environment of the motor vehicle and that can be evaluated within the framework of a sensor fusion to different object information for the individual objects or individual segments of the environment. It is known in this context to use classifiers, in particular as algorithms implemented by software, in order to determine whether an object poses a danger or whether it can be driven over or under in the respective current driving situation. It is known, for example, to use environmental models of the motor vehicle, which models use layout maps and/or are object-based and which models contain the fused and possibly already at least partially evaluated sensor data, which can be differentiated by objects and thus can allow an appropriate classification of objects as an obstacle or not an obstacle. In the process, classifiers may also naturally be used, which allow for a further, more accurate classification of objects so that traffic signs, bollards, curbstones, and the like can, for example, be identified automatically, wherein it is however at least attempted by means of the classifier to determine whether or not the object is an obstacle for the motor vehicle, wherein the classification as an obstacle or not an obstacle can also depend on the current driving situation.
It is however in many cases not trivial to determine whether or not an object constitutes an obstacle. For example, the case can occur that an object is unknown in the classifiers used and thus cannot be assigned at all or cannot be assigned with sufficient certainty. Problems can also occur if a kind of “sensor deception” occurs, i.e. if, for example, an object appearing to be solid is not solid or a color change is not a three-dimensional object. Typical examples for such objects that are hard to identify or can be erroneously classified as an obstacle, for example, are leaves, boxes made of cardboard or paper, darker spots on the road that could be classified as a hole, and the like.
It may in particular occur in the automatic operation of motor vehicles that a current target position or a current destination cannot be reached because on object was erroneously classified as an obstacle or danger. Such problems can occur in the current prior art since an ability to generically and unambiguously interpret general objects cannot as of yet be completely technically realized by the environmental sensor system of the motor vehicle.